A Multi-mode Aging Model and Chaotic Pigeon-Inspired Optimization Particle Filter Algorithm for Remaining Useful Life Prediction of Lithium-ion Batteries

The accurate estimation of the remaining useful life of lithium-ion batteries serves as the basis for the screening and secondary utilization of retired batteries, which is of great significance for ensuring the safety and reliability. Considering the impact of the short-term capacity recovery phenomenon caused by battery storage on the accuracy of aging model characterization, a multi-mode capacity degradation model considering the capacity recovery effect is established, which divides battery aging into three modes: normal degradation, capacity recovery caused by storage, and accelerated degradation after capacity recovery. To address the shortcomings of traditional particle filtering, an intelligent pigeon-inspired algorithm is proposed to optimize the filtering process, and the compass operator and landmark operator are used to enhance the global search ability of particles. In the optimization process of the landmark operator, a chaotic perturbation strategy is added to prevent the algorithm from falling into local optimum. Experimental results show that the established model and estimation strategy improve the RUL estimation error by 50%, and the maximum error of the remaining capacity estimation is kept within 1.75%, demonstrating the accuracy and applicability of the proposed method.